Phylogenetic inference is one of the most challenging and important problems in computational biology. However, computing evolutionary links on data sets containing only few thousands of taxa easily becomes a daunting task. Moreover, recent advances in next-generation sequencing technologies are turning this problem even much harder, either in terms of complexity or scale. Therefore, phylogenetic inference requires new algorithms and methods to handle the unprecedented growth of biological data. In this paper, we identify several types of parallelism that are available while refining a supertree. We also present four improvements that we made to SuperFine - a state-of-the-art supertree (meta)method-, which add support: i) to use FastTree as the inference tool; ii) to use a parallel version of FastTree, or RAxML, as the inference tool; iii) to exploit intra-polytomy parallelism within the so-called polytomy refinement phase; and iv) to exploit, at the same time, inter-polytomy and intra-polytomy parallelism within the polytomy refinement phase. Together, these improvements allow an efficient and transparent exploitation of hybrid-polytomy parallelism. Additionally, we pinpoint how future contributions should enhance the performance of such applications. Our studies show groundbreaking results in terms of the achieved speedups, specially when using biological data sets. Moreover, we show that the new parallel strategy - which exploits the hybrid-polytomy parallelism within the polytomy refinement phase - exhibits good scalability, even in the presence of asymmetric sets of tasks. Furthermore, the achieved results show that the radical improvement in performance does not impair tree accuracy, which is a key issue in phylogenetic inferences.
展开▼
